MANASCI: Management
Science
Introduction
Problem Solving and Decision Making
Quantitative Analysis
Management Science Techniques
Problem Solving and Decision Making
• Problem solving can be defined as the process of identifying the
difference between the actual and the desired state of affairs and
then taking action to resolve the difference.
problem-solving process involved the
following steps
• Identify and define the problem.
• Determine the set of alternative solutions.
• Determine the criterion or criteria that will be used to evaluate the
alternatives.
• Evaluate the alternatives.
• Choose an alternative.
• Implement the selected alternative.
• Evaluate the results to determine whether a satisfactory solution has
been obtained.
• Decision making is the term generally associated with the first five
steps of the problem-solving process. Thus, the first step of decision
making is to identify and define the problem. Decision making ends
with the choosing of an alternative, which is the act of making the
decision.
Types of decision problems
• Single-criterion decision problems are problems in which the
objective is to find the best solution with respect to one criterion.
• Multicriteria decision problems are problems that involve more than
one criterion.
Steps in decision making process
• Structuring the problem.
• Define the problem.
• Identify the alternatives.
• Determine the criteria/criterion.
• Analyzing the problem.
• Evaluate the alternatives.
• Choose an alternative.
Two basic forms of the problem analysis
phase:
• Qualitative Analysis is based primarily on the manager’s judgment
and experience; it includes the manager’s intuitive “feel” for the
problem and more an art than a science.
• Quantitative Analysis focuses on the quantitative facts or data
associated with the problem. It includes the development of
mathematical expressions that describe the objectives, constraints,
and other relationships that exist in the problem. Then, by using one
or more quantitative methods, the analyst will make a
recommendation based on the quantitative aspects of the problem.
Reasons why a quantitative approach might be used
in the decision-making process:
• The problem is complex, and the manager cannot develop a good
solution with the aid of quantitative analysis.
• The problem is especially important, like when a large amount of
money is involved, and the manager desires a thorough analysis
before attempting to make a decision.
• The problem is new, and the manager has no previous experience
from which to draw.
• The problem is repetitive, and the manager saves time and effort by
relying on quantitative procedures to make routine decision
recommendations.
Quantitative Analysis
• Quantitative analysis begins once the problem has been structured.
To successfully apply this to decision making, the management
scientist must work closely with the manager or the user of the
results. Work can begin on developing a model to represent the
problem mathematically.
Models are representations of real objects or
situations and can be presented in various forms.
• An iconic model is a physical replica of a real object.
• An analog model is physical in form but do not have the same
physical appearance as the object being modeled.
• A mathematical model includes the representation of a problem by a
system of symbols and mathematical relationships or expressions. It
is a critical part of any quantitative approach to decision making.
• The total profit from the sale of a product can be determined by
multiplying the profit per unit by the number of units sold. If the
profit per unit of selling smart phones is P500, then the total profit P
for selling x number of units is
P = 500x.
Flowchart of the Process of Transforming
Model Inputs into Output
• Objective Function is a mathematical expression that describes the
problem’s objective.
• Constraints are restrictions such as available of resources, materials
and labor that should be considered in decision making.
• Uncontrollable inputs such as environmental factors which can affect
both the objective function and the constraints. If all uncontrollable
inputs are known and cannot vary, the model is referred to as a
deterministic model. On the other hand, if these are uncertain to the
decision maker, the model is referred to as stochastic or probabilistic
model.
• Controllable inputs are inputs that are completely controlled or
determined by the decision maker. These are the decision alternative
specified by the manager and are also referred to as the decision
variables of the model.
Management Science Techniques
• Linear Programming is a problem solving approach developed for
situations involving maximizing or minimizing a linear function
subjects to linear constraints that limit the degree to which the
objective can be pursued.
• Integer Linear Programming is an approach used for problems that
can be set up as linear programs, with the additional requirement
that some or all of the decision variables be integer values.
• Distribution models are specialized solutions procedures for
problems which can be graphically represented by nodes and arcs.
• Project Scheduling or PERT/CPM are techniques which help managers
carry out their project scheduling responsibilities.
Management Science Techniques
• Waiting Line or Queueing Models are developed to help managers
understand and make better decisions concerning the operation of
systems involving lines.
• Goal Programming is a technique for solving multicriteria decision
problems, usually within the framework of linear programming.
• Forecasting methods are techniques that can be used to predict
future aspects of a business operation.
• Decision analysis can be used to determine optimal strategies in
situations involving several decision alternatives and an uncertain
pattern of future events.
• Markov-process models are useful in studying the evolution of
certain systems over repeated trials (such as describing the
probability that a machine, functioning in one period, will function or
break down in another period).
